In this study, we examined the relationships between the amount of effort identified in review processes and the total amount of effort in end of projects by using data accumulated by a company. We grouped the data by...
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ISBN:
(纸本)9781467396424
In this study, we examined the relationships between the amount of effort identified in review processes and the total amount of effort in end of projects by using data accumulated by a company. We grouped the data by the amount of effort in a review process, and a volume of target projects to find out the relationships. We analyzed the relationship using the Shapiro-Wilk test and Spearman's rank correlation coefficient. The results mean that a volume of the target project can create groups more efficiently to find out a relationship between the amount of effort in review processes and the total amount of effort. They indicate that the total amount of effort can probably be estimated by using the amount of effort in review processes which is grouped by a volume of target projects.
In this paper, we create effort prediction models using self-organizing maps (SOMs)[1] for embedded software development projects. SOMs are a type of artificial neural networks that rely on unsupervised learning. They...
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ISBN:
(纸本)9780769545967
In this paper, we create effort prediction models using self-organizing maps (SOMs)[1] for embedded software development projects. SOMs are a type of artificial neural networks that rely on unsupervised learning. They produce a low-dimensional, discretized representation of the input space of training samples;these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data a multidimensional scaling technique. The advantages of using SOMs for statistical applications are as follows: (1) enabling reasonable inferences to be made from incomplete information via association and recollection, (2) visualizing data, (3) summarizing large-scale data, and (4) creating nonlinear models. We focus on the first advantage to create effort prediction models. To verify our approach, we perform an evaluation experiment that compares SOM models to feedforward artificial neural network (FANN) models using Welch's t test. The results of the comparison indicate that SOM models are more accurate than FANN models for the mean of absolute errors when predicting the amount of effort, because mean errors of the SOM are statistically significantly lower.
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